Mediation analysis methods used in observational research: a scoping review and recommendations

• Published in: BMC medical research methodology 2021-10, Vol.21 (1), p.226-226, Article 226
• Main Authors: Rijnhart, Judith J M, Lamp, Sophia J, Valente, Matthew J, MacKinnon, David P, Twisk, Jos W R, Heymans, Martijn W
• Format: Article
• Language: English
• Subjects: Analysis of variance; Causality; Confidence intervals; Counterfactuals; Direct effect; Epidemiologic methods; Epidemiologic Studies; Estimates; Humans; Indirect effect; Mediation Analysis; Medical research; Methods; Models, Statistical; Observational data; Observational studies; Observational Studies as Topic; Potential outcomes; Regression analysis; Research Design; Research methodology; Software packages; Variables
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-021-01426-3

Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies. We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method. We included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction. To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.